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If we are only using Paracou Biodiversity and Control plots we lack of Symphonia globulifera morphotype to have a balanced stratified sampling (\(\frac{99}{400}\) instead of \(\frac{99}{400}\), see Table 1.1), and we only have 6 S. indet which means we can’t hope they are in fact S. globulifera. Including treatment plots only allow us to reach 168 S. globulifera morphotype but with a lot of S. indet in which we might find other S. globulifera and thus reached a balanced sampling. We observed a strong relation to be tested between water table depth and S. globulifera presence (see Figure 1.1). So we may estimate with the layer the number of S. globulifera alive in Paracou. But including treatment plots necessitate to control for the light variable due to anthropic gaps of the logging. We thus need to test the effect of the treatments and gaps on individuals growth, mortality, and recruitment to evaluate the effect of the treatment on Symphonia Paracou population. So in parrallel we are looking at other Guyafor plots looking for Symphonia globulifera with both diameters inventories for at least three censuses and environmental data.

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Table 2.1 shows few individuals in other Guyafor plots. It is due to the fact that most of Guyafor network plots include few bottom lands (Pascal Petronelli, personnal communication). Consequently it seems if we want both good quality diameter inventories and environment data we will have to sample all individuals in Paracou. The question stay to sample or not in treatment plots ? And more generally the question, is about to know if we will have sufficient environmental data (gaps map, lidar) to control anthropic and natural gaps effects ?

Headcount

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We gathered genetic material (ddRADseq) of french Guiana from Torroba-Balmori unpublished data (Paracou and Regina). We cleaned fastq files after a check with fastQCheck allowing us to correct two sequences by removing theim for individuals \(PR_{49}\) and \(RG_1\). We used ipyrad for the interactive assembly of ddRADseq data sets on genotoul cluster (with denovo assembly, AATT and AT restriction overhang, 85% clustering threshold and a minimum of 48 sample per locus).

We used vcfR to load SNPs data into R, and we transform it in genligh object for adegenet. We related indivdual IDs to their population and coordinates with links table. We coded population in 4 subset for Symphonia globulifera and sp1 in both Paracou and Régina (\(PR_{gl}\), \(PR_{sp}\), \(RG_{gl}\), \(RG_{sp}\)). Population definition was used to transform vcf file to structure file with PGDspider for further genetic structure analysis with STRUCTURE software. We corrected and transformed in UTM coordinates to compute kinship distance matrix with SPAGEDI.

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Morphotype association

Genotype association

Figure 5.1 Symphonia functional triat variation in BRIDGE data (\(n=23\)).

Column 1

I wanted to test for an eventual effect of logging through light and disturbance on Symphonia individuals growth. I used control and treatment plots between 1988 and 1992. I kept only trees already present in 1988 and still alive in 1992, and calculate their growth during this time. I then tried to look at the effect of both distance to the closest logging gaps (\(d_{gaps}\) in \(m\)), original dbh of inidividuals in 1998 (\(dbh_{1998}\) in \(cm\)), and their interaction on growth (\(growth\) in \(cm\)). I used a bayesian model following a log normal law for growth : \[growth \sim log \mathcal{N}(\alpha*log(d_{gaps}+1) + \beta*dbh_{1998} + \gamma*log(d_{gaps}+1)*dbh_{1998}, \sigma) \] The model seems to have correctly converged besides log likelyhood seems to be constrained to a maximal value (see markov chain plot), and parameters seems not much correlated besides a small link between \(\beta\) and \(\gamma\) (see parameters pairs plot). So if we consider the model as valid, parameter posterior distribution seems to indicate a strong effect for the logarithm distance to the gap (\(\alpha\) distribution don’t overlap 0 with a mean at \(\alpha_m = 0.18\)). The growth of Symphonia individuals being increased close to gaps by almost 14% (\(e^{\alpha_m}=1.14\)).

Column 2

\[growth = e^{0.13*log(d_{gaps}+1) + 0.03*dbh_{1988} + -0.01*log(d_{gaps}+1)*dbh_{1988}} \]